Scientific Visualization

Faculteit Science and Engineering
Jaar 2021/22
Vakcode WMCS018-05
Vaknaam Scientific Visualization
Niveau(s) master
Voertaal Engels
Periode semester I b
ECTS 5
Rooster rooster.rug.nl

Uitgebreide vaknaam Scientific Visualization
Leerdoelen At the end of the course, the student is able to:
1. understand the basic principles of scientific visualization, and connect the theory to prior knowledge; understand the context provided by application domains of scientific visualization; understand the mathematical techniques needed in scientific visualization.
2. implement a number of basic scientific visualization techniques, working in a small team; use a suitable programming language and/or toolboxes for implementation.
3. report about the implementations, in terms of algorithms developed, experimental results, and critical analysis.
Omschrijving The aim of this course is to introduce students to the theory and practice of data visualization. After following this course, the students should have a good understanding of: scientific data representation issues (sampling, reconstruction, interpolation, representation in computer models); the structure and operation of the data visualization pipeline; and both theoretical and
implementation-level knowledge of the most frequently used algorithms for scalar, vector, and tensor data visualization. They should be able to select the appropriate algorithms, and algorithm settings, for solving a concrete scientific visualization problem for a given application domain and data source. On a practical side, the students should be able to design and implement the above-mentioned algorithms in an efficient and effective manner in a major programming/scripting language. They should be able to explain the pro's
and con's of the different algorithms for concrete use-cases, and support their explanations with both theoretical and practical arguments.

At the end, students should be familiar with the aims and problems of data visualization, and have a good knowledge of the theory, principles, and methods frequently used in practice in the construction and use of data visualization applications. The course addresses several technical topics, such as: data representation; different types of grids; data sampling, interpolation, and
reconstruction; the concept of a dataset; the visualization pipeline. Several examples are treated, following the different types of visualization data: scalar visualization, vector visualization, tensor visualization.
Uren per week
Onderwijsvorm Hoorcollege (LC), Practisch werk (PRC)
(All lectures and lab sessions are mandatory.)
Toetsvorm Meerkeuze toets (MC), Practisch werk (PR)
(The final grade F for this course is obtained as follows. Let P = mark practicals, E = mark written exam. If E<5 then F=E else F= (E+P)/2. For the resit, Final Grade = resit exam grade. Final grades are rounded to half integers, except for final grades between 5 and 6, which are rounded to integers. To pass the course, a final grade of at least 6 is required.)
Vaksoort master
Coördinator dr. S.D. Frey
Docent(en) dr. S.D. Frey
Verplichte literatuur
Titel Auteur ISBN Prijs
Software examples at
http://www.cs.rug.nl/svcg/DataVisualizationBook
A.C. Telea
Data Visualization - Principles and Practice, 2nd edition; CRC Press; year: 2014 A.C. Telea 9781466585263 €  60,00
Entreevoorwaarden - Linear algebra
- Calculus
- Computer graphics
- General-purpose programming (C,C++, Java, Python)
Opmerkingen This course has limited enrollment:
- CS students can always enter the course, regardless of whether the course is mandatory for them or not.
- The number of enrolments for other non-CS students is limited. These students need to meet the course prerequisite requirements as mentioned on Ocasys. Priority is given to students for which the course is an official elective (see list below).
- An exception can be made for exchange students, if they have a CS background: please contact the FSE International Office. See here for more info about the enrollment procedure.
Opgenomen in
Opleiding Jaar Periode Type
MSc Applied Mathematics: Computational Mathematics  (Computational Mathematics: Guided choice) - semester I b keuzegroep
MSc Artificial Intelligence  (C - Elective Course Units) - semester I b keuze
MSc Astronomy: Quantum Universe  (Optional Courses in Instrumentation & Informatics (I&I)) - semester I b keuze
MSc Astronomy: Quantum Universe  (Optional Courses in Data Science (DS)) - semester I b keuze
MSc Computing Science: Data Science and Systems Complexity  (Compulsory course units) 2 semester I b verplicht
MSc Computing Science: Intelligent Systems and Visual Computing  (Compulsory course units) 1 semester I b verplicht
MSc Computing Science: Science Business and Policy  (Elective course units) 1 semester I b keuze
MSc Computing Science: Software Engineering and Distributed Systems  (Guided choice course units) - semester I b keuze
MSc Courses for Exchange Students: AI - Computing Science - Mathematics - semester I b
MSc Human Machine Communication - per 21-22 Computational Cognitive Science  (C - Elective Course Units) - semester I b keuze
MSc Mathematics: Statistics and Big Data  (Statistics and Big Data: Guided Choice) - semester I b guided choice
MSc Mechanical Engineering: Advanced Instrumentation  (Electives ) 1 semester I b keuze
MSc Mechanical Engineering: Smart Factories  (Electives ) 1 semester I b keuzegroep